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| title: YOLOR | |
| emoji: 🚀 | |
| colorFrom: gray | |
| colorTo: purple | |
| sdk: gradio | |
| app_file: app.py | |
| pinned: false | |
| # Configuration | |
| `title`: _string_ | |
| Display title for the Space | |
| `emoji`: _string_ | |
| Space emoji (emoji-only character allowed) | |
| `colorFrom`: _string_ | |
| Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) | |
| `colorTo`: _string_ | |
| Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) | |
| `sdk`: _string_ | |
| Can be either `gradio` or `streamlit` | |
| `sdk_version` : _string_ | |
| Only applicable for `streamlit` SDK. | |
| See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. | |
| `app_file`: _string_ | |
| Path to your main application file (which contains either `gradio` or `streamlit` Python code). | |
| Path is relative to the root of the repository. | |
| `pinned`: _boolean_ | |
| Whether the Space stays on top of your list. | |
| # YOLOR | |
| implementation of paper - [You Only Learn One Representation: Unified Network for Multiple Tasks](https://arxiv.org/abs/2105.04206) | |
| [](https://paperswithcode.com/sota/real-time-object-detection-on-coco?p=you-only-learn-one-representation-unified) | |
|  | |
| <img src="https://github.com/WongKinYiu/yolor/blob/main/figure/performance.png" height="480"> | |
| To get the results on the table, please use [this branch](https://github.com/WongKinYiu/yolor/tree/paper). | |
| | Model | Test Size | AP<sup>test</sup> | AP<sub>50</sub><sup>test</sup> | AP<sub>75</sub><sup>test</sup> | batch1 throughput | batch32 inference | | |
| | :-- | :-: | :-: | :-: | :-: | :-: | :-: | | |
| | **YOLOR-P6** | 1280 | **54.1%** | **71.8%** | **59.3%** | 49 *fps* | 8.3 *ms* | | |
| | **YOLOR-W6** | 1280 | **55.5%** | **73.2%** | **60.6%** | 47 *fps* | 10.7 *ms* | | |
| | **YOLOR-E6** | 1280 | **56.4%** | **74.1%** | **61.6%** | 37 *fps* | 17.1 *ms* | | |
| | **YOLOR-D6** | 1280 | **57.3%** | **75.0%** | **62.7%** | 30 *fps* | 21.8 *ms* | | |
| | **YOLOR-D6*** | 1280 | **57.8%** | **75.5%** | **63.3%** | 30 *fps* | 21.8 *ms* | | |
| | | | | | | | | | |
| | **YOLOv4-P5** | 896 | **51.8%** | **70.3%** | **56.6%** | 41 *fps* | - | | |
| | **YOLOv4-P6** | 1280 | **54.5%** | **72.6%** | **59.8%** | 30 *fps* | - | | |
| | **YOLOv4-P7** | 1536 | **55.5%** | **73.4%** | **60.8%** | 16 *fps* | - | | |
| | | | | | | | | | |
| To reproduce the inference speed, please see [darknet](https://github.com/WongKinYiu/yolor/tree/main/darknet). | |
| | Model | Test Size | AP<sup>val</sup> | AP<sub>50</sub><sup>val</sup> | AP<sub>75</sub><sup>val</sup> | AP<sub>S</sub><sup>val</sup> | AP<sub>M</sub><sup>val</sup> | AP<sub>L</sub><sup>val</sup> | batch1 throughput | | |
| | :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | | |
| | [**YOLOv4-CSP**](/cfg/yolov4_csp.cfg) | 640 | **49.1%** | **67.7%** | **53.8%** | **32.1%** | **54.4%** | **63.2%** | 76 *fps* | | |
| | [**YOLOR-CSP**](/cfg/yolor_csp.cfg) | 640 | **49.2%** | **67.6%** | **53.7%** | **32.9%** | **54.4%** | **63.0%** | [weights](https://drive.google.com/file/d/1ZEqGy4kmZyD-Cj3tEFJcLSZenZBDGiyg/view?usp=sharing) | | |
| | [**YOLOR-CSP***](/cfg/yolor_csp.cfg) | 640 | **50.0%** | **68.7%** | **54.3%** | **34.2%** | **55.1%** | **64.3%** | [weights](https://drive.google.com/file/d/1OJKgIasELZYxkIjFoiqyn555bcmixUP2/view?usp=sharing) | | |
| | | | | | | | | | |
| | [**YOLOv4-CSP-X**](/cfg/yolov4_csp_x.cfg) | 640 | **50.9%** | **69.3%** | **55.4%** | **35.3%** | **55.8%** | **64.8%** | 53 *fps* | | |
| | [**YOLOR-CSP-X**](/cfg/yolor_csp_x.cfg) | 640 | **51.1%** | **69.6%** | **55.7%** | **35.7%** | **56.0%** | **65.2%** | [weights](https://drive.google.com/file/d/1L29rfIPNH1n910qQClGftknWpTBgAv6c/view?usp=sharing) | | |
| | [**YOLOR-CSP-X***](/cfg/yolor_csp_x.cfg) | 640 | **51.5%** | **69.9%** | **56.1%** | **35.8%** | **56.8%** | **66.1%** | [weights](https://drive.google.com/file/d/1NbMG3ivuBQ4S8kEhFJ0FIqOQXevGje_w/view?usp=sharing) | | |
| | | | | | | | | | |
| Developing... | |
| | Model | Test Size | AP<sup>test</sup> | AP<sub>50</sub><sup>test</sup> | AP<sub>75</sub><sup>test</sup> | AP<sub>S</sub><sup>test</sup> | AP<sub>M</sub><sup>test</sup> | AP<sub>L</sub><sup>test</sup> | | |
| | :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | | |
| | **YOLOR-CSP** | 640 | **51.1%** | **69.6%** | **55.7%** | **31.7%** | **55.3%** | **64.7%** | | |
| | **YOLOR-CSP-X** | 640 | **53.0%** | **71.4%** | **57.9%** | **33.7%** | **57.1%** | **66.8%** | | |
| Train from scratch for 300 epochs... | |
| | Model | Info | Test Size | AP | | |
| | :-- | :-- | :-: | :-: | | |
| | **YOLOR-CSP** | [evolution](https://github.com/ultralytics/yolov3/issues/392) | 640 | **48.0%** | | |
| | **YOLOR-CSP** | [strategy](https://openaccess.thecvf.com/content/ICCV2021W/LPCV/html/Wang_Exploring_the_Power_of_Lightweight_YOLOv4_ICCVW_2021_paper.html) | 640 | **50.0%** | | |
| | **YOLOR-CSP** | [strategy](https://openaccess.thecvf.com/content/ICCV2021W/LPCV/html/Wang_Exploring_the_Power_of_Lightweight_YOLOv4_ICCVW_2021_paper.html) + [simOTA](https://arxiv.org/abs/2107.08430) | 640 | **51.1%** | | |
| | | | | | | |
| | **YOLOR-CSP-X** | [strategy](https://openaccess.thecvf.com/content/ICCV2021W/LPCV/html/Wang_Exploring_the_Power_of_Lightweight_YOLOv4_ICCVW_2021_paper.html) | 640 | **51.5%** | | |
| | **YOLOR-CSP-X** | [strategy](https://openaccess.thecvf.com/content/ICCV2021W/LPCV/html/Wang_Exploring_the_Power_of_Lightweight_YOLOv4_ICCVW_2021_paper.html) + [simOTA](https://arxiv.org/abs/2107.08430) | 640 | **53.0%** | | |
| ## Installation | |
| Docker environment (recommended) | |
| <details><summary> <b>Expand</b> </summary> | |
| ``` | |
| # create the docker container, you can change the share memory size if you have more. | |
| nvidia-docker run --name yolor -it -v your_coco_path/:/coco/ -v your_code_path/:/yolor --shm-size=64g nvcr.io/nvidia/pytorch:20.11-py3 | |
| # apt install required packages | |
| apt update | |
| apt install -y zip htop screen libgl1-mesa-glx | |
| # pip install required packages | |
| pip install seaborn thop | |
| # install mish-cuda if you want to use mish activation | |
| # https://github.com/thomasbrandon/mish-cuda | |
| # https://github.com/JunnYu/mish-cuda | |
| cd / | |
| git clone https://github.com/JunnYu/mish-cuda | |
| cd mish-cuda | |
| python setup.py build install | |
| # install pytorch_wavelets if you want to use dwt down-sampling module | |
| # https://github.com/fbcotter/pytorch_wavelets | |
| cd / | |
| git clone https://github.com/fbcotter/pytorch_wavelets | |
| cd pytorch_wavelets | |
| pip install . | |
| # go to code folder | |
| cd /yolor | |
| ``` | |
| </details> | |
| Colab environment | |
| <details><summary> <b>Expand</b> </summary> | |
| ``` | |
| git clone https://github.com/WongKinYiu/yolor | |
| cd yolor | |
| # pip install required packages | |
| pip install -qr requirements.txt | |
| # install mish-cuda if you want to use mish activation | |
| # https://github.com/thomasbrandon/mish-cuda | |
| # https://github.com/JunnYu/mish-cuda | |
| git clone https://github.com/JunnYu/mish-cuda | |
| cd mish-cuda | |
| python setup.py build install | |
| cd .. | |
| # install pytorch_wavelets if you want to use dwt down-sampling module | |
| # https://github.com/fbcotter/pytorch_wavelets | |
| git clone https://github.com/fbcotter/pytorch_wavelets | |
| cd pytorch_wavelets | |
| pip install . | |
| cd .. | |
| ``` | |
| </details> | |
| Prepare COCO dataset | |
| <details><summary> <b>Expand</b> </summary> | |
| ``` | |
| cd /yolor | |
| bash scripts/get_coco.sh | |
| ``` | |
| </details> | |
| Prepare pretrained weight | |
| <details><summary> <b>Expand</b> </summary> | |
| ``` | |
| cd /yolor | |
| bash scripts/get_pretrain.sh | |
| ``` | |
| </details> | |
| ## Testing | |
| [`yolor_p6.pt`](https://drive.google.com/file/d/1Tdn3yqpZ79X7R1Ql0zNlNScB1Dv9Fp76/view?usp=sharing) | |
| ``` | |
| python test.py --data data/coco.yaml --img 1280 --batch 32 --conf 0.001 --iou 0.65 --device 0 --cfg cfg/yolor_p6.cfg --weights yolor_p6.pt --name yolor_p6_val | |
| ``` | |
| You will get the results: | |
| ``` | |
| Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.52510 | |
| Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.70718 | |
| Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.57520 | |
| Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.37058 | |
| Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.56878 | |
| Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66102 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.39181 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.65229 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.71441 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.57755 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.75337 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.84013 | |
| ``` | |
| ## Training | |
| Single GPU training: | |
| ``` | |
| python train.py --batch-size 8 --img 1280 1280 --data coco.yaml --cfg cfg/yolor_p6.cfg --weights '' --device 0 --name yolor_p6 --hyp hyp.scratch.1280.yaml --epochs 300 | |
| ``` | |
| Multiple GPU training: | |
| ``` | |
| python -m torch.distributed.launch --nproc_per_node 2 --master_port 9527 train.py --batch-size 16 --img 1280 1280 --data coco.yaml --cfg cfg/yolor_p6.cfg --weights '' --device 0,1 --sync-bn --name yolor_p6 --hyp hyp.scratch.1280.yaml --epochs 300 | |
| ``` | |
| Training schedule in the paper: | |
| ``` | |
| python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train.py --batch-size 64 --img 1280 1280 --data data/coco.yaml --cfg cfg/yolor_p6.cfg --weights '' --device 0,1,2,3,4,5,6,7 --sync-bn --name yolor_p6 --hyp hyp.scratch.1280.yaml --epochs 300 | |
| python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 tune.py --batch-size 64 --img 1280 1280 --data data/coco.yaml --cfg cfg/yolor_p6.cfg --weights 'runs/train/yolor_p6/weights/last_298.pt' --device 0,1,2,3,4,5,6,7 --sync-bn --name yolor_p6-tune --hyp hyp.finetune.1280.yaml --epochs 450 | |
| python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train.py --batch-size 64 --img 1280 1280 --data data/coco.yaml --cfg cfg/yolor_p6.cfg --weights 'runs/train/yolor_p6-tune/weights/epoch_424.pt' --device 0,1,2,3,4,5,6,7 --sync-bn --name yolor_p6-fine --hyp hyp.finetune.1280.yaml --epochs 450 | |
| ``` | |
| ## Inference | |
| [`yolor_p6.pt`](https://drive.google.com/file/d/1Tdn3yqpZ79X7R1Ql0zNlNScB1Dv9Fp76/view?usp=sharing) | |
| ``` | |
| python detect.py --source inference/images/horses.jpg --cfg cfg/yolor_p6.cfg --weights yolor_p6.pt --conf 0.25 --img-size 1280 --device 0 | |
| ``` | |
| You will get the results: | |
|  | |
| ## Citation | |
| ``` | |
| @article{wang2021you, | |
| title={You Only Learn One Representation: Unified Network for Multiple Tasks}, | |
| author={Wang, Chien-Yao and Yeh, I-Hau and Liao, Hong-Yuan Mark}, | |
| journal={arXiv preprint arXiv:2105.04206}, | |
| year={2021} | |
| } | |
| ``` | |
| ## Acknowledgements | |
| <details><summary> <b>Expand</b> </summary> | |
| * [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet) | |
| * [https://github.com/WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) | |
| * [https://github.com/WongKinYiu/ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) | |
| * [https://github.com/ultralytics/yolov3](https://github.com/ultralytics/yolov3) | |
| * [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5) | |
| </details> | |